Intrinsic Relationship Research Articles

Performance analysis of metallo-dielectric optical filters designed using dispersion relations

We present an experimental validation of an analytical approach to predict the spectral response of multilayer metallo-dielectric structures, with applications in the efficient design of bandpass optical filters. Instead of relying on trial-and-error methods, our approach—referred to as the dispersion relation method—is based on intrinsic physical relations, potentially resulting in a significant reduction in time and resources during the design and optimization process. In our previous work, we illustrated that characteristics of the band structure revealed by the dispersion relation can serve as reasonable estimates for the center wavelengths and bandwidths for finite multilayer structures, as obtained from numerical simulations. In this work, we verify those conclusions with experimental results from metallo-dielectric optical filters prepared via magnetron sputtering. Structures using different metals including Ag, Au, and Al are investigated. We show good agreement between spectral response predictions derived from dispersion relations, numerical simulations using the transfer matrix method, and experimental transmittance spectra of the fabricated structures. Considering this experimental validation, the analytical approach based on dispersion relations can offer an accurate and efficient method for designing metallo-dielectric filter structures. Our approach facilitates the selection of geometric parameters for fabrication and provides valuable insight into the optical characteristics of such structures.

Study of Grain Boundary: From Crystallization Engineering to Machine Learning

Grain boundaries play a vital role in determining the structural, functional, mechanical, and electrical properties of semiconductor materials. Recent studies have yielded great advances in understanding and modulating the grain boundaries via semiconductor crystallization engineering and machine learning. In this article, we first provide a review of the miscellaneous methods and approaches that effectively control the nucleation formation, semiconductor crystallization, and grain boundary of organic semiconductors. Using the benchmark small molecular semiconductor 6,13-bis(triisopropylsilylethynyl) pentacene (TIPS pentacene) as a representative example, the crystallization engineering methods include polymer additive mixing, solvent annealing, gas injection, and substrate temperature control. By studying the grain-width-dependent charge transport, we propose a grain boundary model as a fundamental basis to theoretically understand the intrinsic relation between grain boundary engineering and charge carrier mobility. Furthermore, we discuss the various machine learning algorithms and models used to analyze grain boundaries for the various important traits and properties, such as grain boundary crystallography, energy, mobility, and dislocation density. This work highlights the unique advantages of both crystallization engineering and machine learning methods, demonstrates new insights into discovering the presence of grain boundaries and understanding new properties of materials, and sheds light on the great potential of material application in various fields, such as organic electronics.

The relationship of spatial visualization ability and number representation: evidence from multiple tasks

It is well established in the literature that the relation of spatial ability and the number representation, but the intrinsic relation of spatial visualization ability and number representation are not well understood. In the Current study, Chinese Preschool children (N = 200; 107 girls; Mage = 5.47years, SD = 0.67) completed two kinds of spatial visualization tasks and six kinds of number representation tasks. The results showed that spatial visualization ability was positively correlated with number representation for 5 years-old Children. Furthermore, spatial visualization ability can positively predicted number representation. These findings are consistent with the view that spatial visualization is recognized for its potential to enhance numerical skills, encompassing competencies associated with fundamental number. Therefore, developing children’s spatial visualization ability may be an effective way to enhance their number representation skills. This has important implications for early education and intervention strategies.

Face Boundary Formulation for Harmonic Models: Face Image Resembling.

This paper is devoted to numerical algorithms based on harmonic transformations with two goals: (1) face boundary formulation by blending techniques based on the known characteristic nodes and (2) some challenging examples of face resembling. The formulation of the face boundary is imperative for face recognition, transformation, and combination. Mapping between the source and target face boundaries with constituent pixels is explored by two approaches: cubic spline interpolation and ordinary differential equation (ODE) using Hermite interpolation. The ODE approach is more flexible and suitable for handling different boundary conditions, such as the clamped and simple support conditions. The intrinsic relations between the cubic spline and ODE methods are explored for different face boundaries, and their combinations are developed. Face combination and resembling are performed by employing blending curves for generating the face boundary, and face images are converted by numerical methods for harmonic models, such as the finite difference method (FDM), the finite element method (FEM) and the finite volume method (FVM) for harmonic models, and the splitting-integrating method (SIM) for the resampling of constituent pixels. For the second goal, the age effects of facial appearance are explored to discover that different ages of face images can be produced by integrating the photos and images of the old and the young. Then, the following challenging task is targeted. Based on the photos and images of parents and their children, can we obtain an integrated image to resemble his/her current image as closely as possible? Amazing examples of face combination and resembling are reported in this paper to give a positive answer. Furthermore, an optimal combination of face images of parents and their children in the least-squares sense is introduced to greatly facilitate face resembling. Face combination and resembling may also be used for plastic surgery, finding missing children, and identifying criminals. The boundary and numerical techniques of face images in this paper can be used not only for pattern recognition but also for face morphing, morphing attack detection (MAD), and computer animation as Sora to greatly enhance further developments in AI.

Fundamentals and Challenges of Ligand Modification in Heterogeneous Electrocatalysis.

The development of efficient catalytic materials in the energy field could promote the structural transformation from traditional fossil fuels to sustainable energy. In heterogeneous catalytic reactions, ligand modification is an effective way to regulate both electronic and steric structures of catalytic sites, thus paving a prospective avenue to design the interfacial structures of heterogeneous catalysts for energy conversion. Although great achievements have been obtained for the study and applications of heterogeneous ligand-modified catalysts, the systematical refinements of ligand modification strategies are still lacking. Here, we reviewed the ligand modification strategy from both the mechanistic and applicable scenarios by focusing on heterogeneous electrocatalysis. We elucidated the ligand-modified catalysts in detail from the perspectives of basic concepts, preparation, regulation of physicochemical properties of catalytic sites, and applications in different electrocatalysis. Notably, we bridged the electrocatalytic performance with the electronic/steric effects induced by ligand modification to gain intrinsic structure-performance relations. We also discussed the challenges and future perspectives of ligand modification strategies in heterogeneous catalysis.

Fully exploring object relation interaction and hidden state attention for video captioning

Video Captioning (VC) is a challenging task of automatically generating natural language sentences for describing video contents. As a video often contains multiple objects, it is comprehensively crucial to identify multiple objects and model relationships between them. Previous models usually adopt Graph Convolutional Networks (GCN) to infer relational information via object nodes, but there exist uncertainty and over-smoothing issues of relational reasoning. To tackle these issues, we propose a Knowledge Graph based Video Captioning Network (KG-VCN) by fully exploring object relation interaction, hidden state and attention enhancement. In encoding stages, we present a Graph and Convolution Hybrid Encoder (GCHE), which uses an object detector to find visual objects with bounding boxes for Knowledge Graph (KG) and Convolutional Neural Network (CNN). To model intrinsic relations between detected objects, we propose a knowledge graph based Object Relation Graph Interaction (ORGI) module. In ORGI, we design triplets (head, relation, tail) to efficiently mine object relations, and create a global node to enable adequate information flow among all graph nodes for avoiding possibly missed relations. To produce accurate and rich captions, we propose a hidden State and Attention Enhanced Decoder (SAED) by integrating hidden states and dynamically updated attention features. Our SAED accepts both relational and visual features, adopts Long Short-Term Memory (LSTM) to produce hidden states, and dynamically update attention features. Unlike existing methods, we concatenate state and attention features to predict next word sequentially. To demonstrate the effectiveness of our model, we conduct experiments on three well-known datasets (MSVD, MSR-VTT, VaTeX), and our model achieves impressive results significantly outperforming existing state-of-the-art models.

The quantum uncertainty relation for skew information of coherence

Abstract The quantum uncertainty relation characterizes the restriction of the quantum coherence on different measurement bases. The skew information of coherence is a coherence measure both operationally and informatically. We study the quantum uncertainty relation for the skew information of coherence under any two orthonormal bases. In qubit and qutrit systems, we derive the quantum uncertainty relations with the lower bounds in the factorized forms by the purity of quantum states and the incompatibility between two orthonormal bases. Especially the lower bound in qubit systems is strictly positive for non-maximally mixed states and incompatible orthonormal bases. These quantum uncertainty relations reveal the intrinsic relation among the coherence, the purity and the incompataibility between two orthonormal bases quantitatively. The advantages of these quantum uncertainty relations are illustrated by some specific examples. This method we developed here is beneficial to some other quantum uncertainty relations.

Optimality of the Howard-Vala T-gate in stabilizer quantum computation

In a remarkable work [Phys. Rev. A 86 022316 (2012)], Howard and Vala introduced a qudit version of the qubit T-gate (i.e., π/8-gate) for any prime dimensional system. This non-Clifford gate is a key ingredient of the paradigm ‘Clifford +T’, which are widely employed in the stabilizer formalism of universal and fault-tolerant quantum computation. Considering the applications and significance of the T-gate, it is desirable to characterize it from various angles. Here we prove that in any prime dimensional system, the Howard-Vala T-gate is optimal, among all diagonal gates, for generating magic resources from stabilizer states when the magic is quantified via the L 1-norm of characteristic functions (Fourier transforms) of quantum states. The quadratic Gaussian sum in number theory plays a key role in establishing this optimality. This highlights an extreme feature of the Howard-Vala T-gate. We further reveal an intrinsic relation between the Howard-Vala T-gate and the Watson-Campbell-Anwar-Browne T-gate [Phys. Rev. A 92 022312 (2015)] for any prime dimensional system.

Computational fluid dynamics investigation of pesticide spraying by agricultural drones

Precise control over pesticide spraying by agricultural drones requires fundamental research of multiphase flow, heat and mass transfer of liquid droplets in downwash flow. In this study, water was used as a carrier agent in pesticide formulations, in which the liquid droplets exhibit a Newtonian fluid behavior. A general computational fluid dynamics (CFD) model that characterizes propeller motion coupled with droplet evaporation and deposition was developed. The downwash flow generated by the propeller rotation was simulated using a multiple reference frame method along with the SST-k-ω turbulence model. Spraying of liquid droplets was simulated using a discrete phase model. The flow model validation was conducted by comparing the simulated velocities with experimental data, and the spray model validation was completed by a comparison of droplet depositions from CFD predictions and experiments. The results indicated that the droplet deposition fluctuates with an increase of propeller speed, and that both temperature and relative humidity affect the droplet deposition. In addition, the relation between the number of particles and the impact velocity was identified quantitatively, impact velocity with respect to particle diameter was predicted, and the effects of surface inclination, surface roughness, and crosswind speed on droplet deposition were analyzed. Moreover, an in-depth discussion about the intrinsic relation between downwash flow and droplet spraying was conducted.

Measuring linear birefringence via rotating-sample transmission Stokes spectropolarimetry

Linear birefringence is a fundamental property of optically anisotropic media, defined by the difference in refractive index experienced by light polarized along orthogonal directions. It is usually manifested in microscopically aligned molecular systems, where a preferential direction of light–matter interaction is created. For instance, the anisotropic structure of calcite crystal causes the famous double-refraction phenomenon. Another common example is commercial adhesive tapes, which are polymeric materials possessing birefringent properties due to their manufacturing processes. The intrinsic relation between birefringence and molecular alignment forges a new analytical route to study materials such as polymeric thin films. Therefore, the capacity of measuring linear birefringence and its fast axis is of paramount importance for the science of anisotropic molecular systems. In this contribution, a comprehensive approach to acquire linear birefringence using rotating-sample transmission Stokes spectropolarimetry is presented and applied to transparent adhesive tapes as a case study. The experimental setup comprises a thermal light source and a spectropolarimeter capable of determining wavelength distributions of Stokes parameters. The samples are carefully aligned in a rotating mount and subjected to a fixed broadband vertically polarized light beam. Then, the transmitted light is analyzed using a rotating retarder type of spectropolarimeter. Through systematic variation of the sample’s angular position, the Stokes parameters of transmitted light are measured for each transmitted wavelength as a function of the sample’s angular position. The linear retardance and fast axis direction relative to the tape’s long axis are then determined from the modulation of Stokes parameters over sample rotation. The model derivation, experimental procedure, and signal processing protocol are described in detail, and the approach is verified with a simple correlation between linear retardance and the number of stacked layers of tape.

Revealing of Intrinsic Relation between Surface Atomic Geometry and Catalytic Mechanism of CuNi Alloy Catalyst in CO2 Hydrogenation to Methanol

Revealing of Intrinsic Relation between Surface Atomic Geometry and Catalytic Mechanism of CuNi Alloy Catalyst in CO₂ Hydrogenation to Methanol

Job Satisfaction and Generational Difference: The Shifting Nature of the Workplace

This study investigates job satisfaction across different generational cohorts within the workforce. It explores how intrinsic rewards, extrinsic rewards, work relations, work-life balance, and employee activation variables influence job satisfaction among these groups. A web-based survey was conducted across the United States, involving 566 participants. Regression analysis was used to identify significant predictors of job satisfaction for each generational cohort. The results revealed that interesting work significantly predicts job satisfaction for all generations, with the strongest impact observed in Generation Z. baby boomers, Generation X, and millennials reported high levels of satisfaction with pay and relations with management. The study highlights distinct generational preferences, contributing to a nuanced understanding of job satisfaction drivers.

Intrinsic relations among chemistry, physics and mathematics in the brazilian high school: a theoretical-methodological approach

This paper reports an investigation into the dialogical relation among significant learning experiences of Chemistry, Physics and Mathematics contents in an interdisciplinary context. It aimed at answering the following questions that are relevant to the field of Exact Sciences: why do students show little interest in these courses?; where is the problem?; did Chemistry and Physics abandon Mathematics when they started to focus more on qualitative and theoretical concepts than on quantitative ones?; and how can the three courses work together to make both teaching and learning processes easier?. This study presented an actual overview of the Brazilian High School by highlighting strengths and weaknesses from the perspective of Chemistry, Physics and Mathematics teaching. The Brazilian educational scenario showed again that the three courses are the ones that students enjoy the least. The research methodology had a qualitative focus since it had an interpretative and descriptive nature. Answers given to the questions may be found in the text intertwined with some worrisome reflections, which also include the triad teacher-student-educational system. Results showed that further studies are needed to find out the reasons for multiple difficulties found in the intrinsic relations among Chemistry, Physics and Mathematics. It is disappointing to know that no satisfactory practical solution has been proposed so far. Everything has been kept in the educational ideal described by research articles, chapters and motivational quotes.

The Changing Substance of European Law

Intrinsic relation between form and substance in law and between the institutional law of the EU and its substantive goals – idea and criteria for a ‘substantive core’ to the legal order – the internal market as early EU law’s substantive core – the EU’s changing policy substance and the subsequent ‘de-coring’ of the legal order – the consequences of de-coring and its impact on EU law’s relevance and legitimacy – early attempts to reconcile EU law with the changing substance of EU policy.

The intrinsic scaling relation between pressure fluctuations and Mach number in compressible turbulent boundary layers

The intrinsic scaling relation between pressure fluctuations and Mach number in compressible turbulent boundary layers

Drug repositioning by collaborative learning based on graph convolutional inductive network

Motivation:Computational drug repositioning is a vital path to improve efficiency of drug discovery, which aims to find potential Drug–Disease Associations (DDAs) to develop new effects of the existing drugs. Many approaches detected novel DDAs from heterogenous network which integrates similar drugs, similar diseases and the known DDAs. However, sparsity of the known DDAs and intrinsic synergic relations on representations of drugs and diseases in the heterogenous network are still the main challenges for DDAs prediction. Results:To address the problems, a novel drug repositioning approach is proposed here. Firstly, a drug similar network is constructed by Gaussian similarity kernel fusion of multisource drug similarities. Likewise, a disease similar network is generated by the same strategy. Secondly, the known DDAs network is extended by a bi-random walk algorithm from the above-mentioned similar networks. Meanwhile, representations of drugs and diseases are learned from their similar networks through graph convolutions and then a DDAs network is induced from the representations. Finally, to discover latent DDAs, the inductive DDAs network is refined iteratively by collaborating with the extended known DDAs network. Comprehensive experimental results show that our method outperforms several state-of-the-art methods for predicting DDAs on indicators including precision, recall, F1-score, MCC, ROC and AUPR. Moreover, case studies suggest that our method is highly effective in practices. The success of our method may be attributed to three aspects: (1) reliable similar relationships of drugs and diseases; (2) enhanced connectivity of the heterogenous network; (3) reasonable collaborative induction on DDAs network. Our method is freely available at https://github.com/BioMLab/DRCLN.

Specification tests for normal/gamma and stable/gamma stochastic frontier models based on empirical transforms

Goodness–of–fit tests for the distribution of the composed error term in a Stochastic Frontier Model (SFM) are suggested. The focus is on the case of a normal/gamma SFM and the heavy–tailed stable/gamma SFM. In the first case the moment generating function is used as tool while in the latter case the characteristic function of the error term is employed. In both cases our test statistics are formulated as weighted integrals of properly standardized data. The new normal/gamma test is consistent, and is shown to have an intrinsic relation to moment–based tests. The finite–sample behavior of resampling versions of both tests is investigated by Monte Carlo simulation, while several real–data applications are also included.

Machine learning modeling of the capacitive performance of N-doped porous biochar electrodes with experimental verification

N-doped porous biochar is considered as a promising carbon material for supercapacitor electrodes application. However, the intrinsic relations and effect mechanisms of the pore structure and N-doping to the capacitive performance are still inscrutable, giving rise to the challenges for enhancing the capacitive performance by regulating the physicochemical properties of N-doped biochar. In this study, various machine learning models were established to predict the specific capacitance of N-doped biochar electrodes based on the pore structure and N-doping properties. The effect mechanisms of pore structure and N-doping to the specific capacitance were also explored. Results showed that Random Forest model predicted the specific capacitance most accurately. The generalization performance of the model was verified to be quite well with our experiments. It is suggested that developing pore structure with abundant micropores plays more important role than N-doping in enhancing the specific capacitance. The optimal interval of each physiochemical property of N-doped biochar were also determined to maximize the specific capacitance. Furthermore, synergistic effects of pore structure and N-doping to the specific capacitance were revealed. This study provides a useful guideline for N-doped porous biochar production with the aim of capacitive performance enhancement.

Hematological toxicity of disinfection by-product iodoacetic acid

To clarify the effect of iodoacetic acid(IAA) on the blood system and electrolyte balance, hence further study the intrinsic relation of blood routine parameters and electrolyte levels, major hematological toxicity effects and their pattern after IAA treatment. Forty-eight 21-day-old male SPF grade Sprague-Dawley(SD) rats were gavaged with 0, 6.25, 12.5 and 25 mg/kg IAA for 31 days. After detections of blood routine and plasma inorganic ion levels, Spearman correlation coefficients were performed to evaluate their relationship. Changes in ferritin, transferrin, hepcidin, C-reactive protein and glyceraldehyde-3-phosphate dehydrogenase(GAPDH) were assessed by enzyme-linked immunosorbent assays. The EDock bioinformatics tool was applied to docking model of IAA and GAPDH. Compared to the control, high-dose IAA exposure had obvious inhibition effect on rat leukocytes with the total number declined by 51.12%, and neutrophils were particularly sensitive to IAA with the number reduced by 73.66%(P<0.01), and rat erythrocytes exhibited a small cell low pigment effect with hemoglobin and hematocrit decreased by 8.60% and 8.70%, respectively(P<0.05). But IAA had little effects on the platelet. Plasma iron, phosphorus, zinc and potassium levels were repressed significantly, while chlorine, sodium and magnesium levels were elevated obviously through IAA exposure. However, plasma calcium levels were hardly affected by IAA. In comparison with the control, iron levels declined by 67.09%, whereas magnesium levels increased by 131.82% in the high-dose group(P<0.01). Overall, correlation analyses uncovered that plasma iron metabolism was most strongly and positively correlated with levels of leukocyte, erythrocyte and platelet system parameters after IAA exposure, and the correlation coefficients of leukocyte number, mean hemoglobin content and mean erythrocyte volume were 0.637, 0.410 and 0.365, respectively(P<0.05). Compared to the control, in the high-dose IAA group, the plasma content of C-reactive protein was significantly upregulated by 13.30%(P<0.05), and plasma levels of transferrin and ferromodulin were also respectively elevated by 12.73% and 11.02%(P<0.05). But plasma levels of ferritin and GAPDH did not differ between groups. The docking model exhibited that IAA could bind to the 150 Cys active site of rat GAPDH did. IAA not only had toxic effects on rat leukocytes and the plasma electrolyte balance, but also generated inflammation and iron deficiency, leading to smaller erythrocytes and lower pigment.

Improving metrology with quantum scrambling in a spin-1 Bose-Einstein condensate coupled to a cavity.

Spinor Bose-Einstein condensate is an ideal candidate for implementing the many-body entanglement, quantum measurement and quantum information processing owing to its inherent spin-mixing dynamics. Here we present a system of an 87Rb atomic spin-1 Bose-Einstein condensate coupled to an optical ring cavity, in which cavity-mediated nonlinear interactions give rise to saddle points in the semiclassical phase space, providing a general mechanism for exponential fast scrambling and metrological gain augment. We theoretically study metrological gain and fidelity out-of-time-ordered correlator based on time-reversal protocols and demonstrate that exponential rapid scrambling dynamics can enhance quantum metrology. In addition, we use the out-of-time-ordered correlator to probe dynamical phase transitions. This work is useful to understand the intrinsic relation between the concepts from different subfields of quantum science.